{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"08_Neural_Networks","provenance":[{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb","timestamp":1608053133778},{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb","timestamp":1582822364411}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"hVPVtBmT2lxP"},"source":["<div align=\"center\">\n","<h1><img width=\"30\" src=\"https://madewithml.com/static/images/rounded_logo.png\">&nbsp;<a href=\"https://madewithml.com/\">Made With ML</a></h1>\n","Applied ML · MLOps · Production\n","<br>\n","Join 30K+ developers in learning how to responsibly <a href=\"https://madewithml.com/about/\">deliver value</a> with ML.\n","    <br>\n","</div>\n","\n","<br>\n","\n","<div align=\"center\">\n","    <a target=\"_blank\" href=\"https://newsletter.madewithml.com\"><img src=\"https://img.shields.io/badge/Subscribe-30K-brightgreen\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://github.com/GokuMohandas/MadeWithML\"><img src=\"https://img.shields.io/github/stars/GokuMohandas/MadeWithML.svg?style=social&label=Star\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://www.linkedin.com/in/goku\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://twitter.com/GokuMohandas\"><img src=\"https://img.shields.io/twitter/follow/GokuMohandas.svg?label=Follow&style=social\"></a>\n","    <br>\n","    🔥&nbsp; Among the <a href=\"https://github.com/topics/deep-learning\" target=\"_blank\">top ML</a> repositories on GitHub\n","</div>\n","\n","<br>\n","<hr>"]},{"cell_type":"markdown","metadata":{"id":"eTdCMVl9YAXw"},"source":["# Neural Networks\n","\n","In this lesson, we will explore multilayer perceptrons (MLPs) which are a basic type of neural network. We'll first motivate non-linear activation functions by trying to fit a linear model (logistic regression) on our non-linear spiral data. Then we'll implement an MLP using just NumPy and then with PyTorch."]},{"cell_type":"markdown","metadata":{"id":"xuabAj4PYj57"},"source":["<div align=\"left\">\n","<a target=\"_blank\" href=\"https://madewithml.com/courses/basics/neural-networks/\"><img src=\"https://img.shields.io/badge/📖 Read-blog post-9cf\"></a>&nbsp;\n","<a href=\"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\" role=\"button\"><img src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"VoMq0eFRvugb"},"source":["# Overview"]},{"cell_type":"markdown","metadata":{"id":"qWro5T5qTJJL"},"source":["Our goal is to learn a model $\\hat{y}$ that models $y$ given $X$ . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/images/basics/neural-networks/mlp.png\" width=\"500\">\n","</div>\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"JqxyljU18hvt"},"source":["* **Objective:**  Predict the probability of class $y$ given the inputs $X$. Non-linearity is introduced to model the complex, non-linear data.\n","* **Advantages:**\n","  * Can model non-linear patterns in the data really well.\n","* **Disadvantages:**\n","  * Overfits easily.\n","  * Computationally intensive as network increases in size.\n","  * Not easily interpretable.\n","* **Miscellaneous:** Future neural network architectures that we'll see use the MLP as a modular unit for feed forward operations (affine transformation (XW) followed by a non-linear operation)."]},{"cell_type":"markdown","metadata":{"id":"3GHB1Qi3sskB"},"source":["> We're going to leave out the bias terms $\\beta$ to avoid further crowding the backpropagation calculations."]},{"cell_type":"markdown","metadata":{"id":"9vbZa-cxlujX"},"source":["# Set up"]},{"cell_type":"code","metadata":{"id":"uQNbn5-LFO7w"},"source":["import numpy as np\n","import random"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_cRUTo0hFO-i"},"source":["SEED = 1234"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EU0SEgIFFPA-"},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)\n","random.seed(SEED)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XtKqNioAayCy"},"source":["## Load data"]},{"cell_type":"markdown","metadata":{"id":"X3OrtMpFayFC"},"source":["I created some non-linearly separable spiral data so let's go ahead and download it for our classification task."]},{"cell_type":"code","metadata":{"id":"9NfIz_4OPYpG"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"efS3lVYETA17","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1608144412889,"user_tz":420,"elapsed":1382,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"d5bfe7e8-adbb-48c5-a915-cc43f4a6fc41"},"source":["# Load data\n","url = \"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/datasets/spiral.csv\"\n","df = pd.read_csv(url, header=0) # load\n","df = df.sample(frac=1).reset_index(drop=True) # shuffle\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","      <th>color</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.106737</td>\n","      <td>0.114197</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.311513</td>\n","      <td>-0.664028</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.019870</td>\n","      <td>-0.703126</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.054017</td>\n","      <td>0.508159</td>\n","      <td>c3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.127751</td>\n","      <td>-0.011382</td>\n","      <td>c3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         X1        X2 color\n","0  0.106737  0.114197    c1\n","1  0.311513 -0.664028    c1\n","2  0.019870 -0.703126    c1\n","3 -0.054017  0.508159    c3\n","4 -0.127751 -0.011382    c3"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"jmTwuA9kHjhA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144412890,"user_tz":420,"elapsed":1366,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"651df414-5f2e-417a-beb5-cc66924c7a45"},"source":["# Data shapes\n","X = df[['X1', 'X2']].values\n","y = df['color'].values\n","print (\"X: \", np.shape(X))\n","print (\"y: \", np.shape(y))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X:  (1500, 2)\n","y:  (1500,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jgVjStv8VnX2","colab":{"base_uri":"https://localhost:8080/","height":281},"executionInfo":{"status":"ok","timestamp":1608144413399,"user_tz":420,"elapsed":1858,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1961a014-1a94-46a3-8a61-94cd6db93c9b"},"source":["# Visualize data\n","plt.title(\"Generated non-linear data\")\n","colors = {'c1': 'red', 'c2': 'yellow', 'c3': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], edgecolors='k', s=25)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"f01ldQXXS3lc"},"source":["## Split data"]},{"cell_type":"markdown","metadata":{"id":"-wZmsNeUf7Ky"},"source":["We'll shuffle our dataset (since it's ordered by class) and then create our data splits (stratified on class)."]},{"cell_type":"code","metadata":{"id":"a7HwlgP9-G52"},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wRVOgVxDDcx3"},"source":["TRAIN_SIZE = 0.7\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ad9RNqcQf2cA"},"source":["def train_val_test_split(X, y, train_size):\n","    \"\"\"Split dataset into data splits.\"\"\"\n","    X_train, X_, y_train, y_ = train_test_split(X, y, train_size=TRAIN_SIZE, stratify=y)\n","    X_val, X_test, y_val, y_test = train_test_split(X_, y_, train_size=0.5, stratify=y_)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gGFqcqTDXhkl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144414313,"user_tz":420,"elapsed":2750,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e1c918fc-5843-474a-ca34-b39cbb38b38d"},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, train_size=TRAIN_SIZE)\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train: (1050, 2), y_train: (1050,)\n","X_val: (225, 2), y_val: (225,)\n","X_test: (225, 2), y_test: (225,)\n","Sample point: [-0.63919105 -0.69724176] → c1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"w8EQEwO2OWfN"},"source":["## Label encoding"]},{"cell_type":"markdown","metadata":{"id":"6fd9d-NSgH6D"},"source":["In the previous lesson we wrote our own label encoder class to see the inner functions but this time we'll use scikit-learn [`LabelEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html) class which does the same operations as ours."]},{"cell_type":"code","metadata":{"id":"0ldFOhAqObcd"},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mYjAYlTdObf0"},"source":["# Output vectorizer\n","label_encoder = LabelEncoder()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VO1uxrdgObkf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144414316,"user_tz":420,"elapsed":2734,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"da263506-485f-4867-d9cf-f420288badb5"},"source":["# Fit on train data\n","label_encoder = label_encoder.fit(y_train)\n","classes = list(label_encoder.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classes: ['c1', 'c2', 'c3']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1t9vjih9Oeq9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144414316,"user_tz":420,"elapsed":2719,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"10f3056c-756d-4bc1-e7e0-f8c7a0186cbd"},"source":["# Convert labels to tokens\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = label_encoder.transform(y_train)\n","y_val = label_encoder.transform(y_val)\n","y_test = label_encoder.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["y_train[0]: c1\n","y_train[0]: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QVgNHgSkInPE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144414317,"user_tz":420,"elapsed":2705,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"f26dcec4-f612-4773-ff85-978ca6c89c87"},"source":["# Class weights\n","counts = np.bincount(y_train)\n","class_weights = {i: 1.0/count for i, count in enumerate(counts)}\n","print (f\"counts: {counts}\\nweights: {class_weights}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["counts: [350 350 350]\n","weights: {0: 0.002857142857142857, 1: 0.002857142857142857, 2: 0.002857142857142857}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MDFM5gQte5rQ"},"source":["## Standardize data"]},{"cell_type":"markdown","metadata":{"id":"JlmiwCUre_ZH"},"source":["We need to standardize our data (zero mean and unit variance) so a specific feature's magnitude doesn't affect how the model learns its weights. We're only going to standardize the inputs X because our outputs y are class values."]},{"cell_type":"code","metadata":{"id":"D4uCG3vMe52J"},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3tTbwUOme5z7"},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zGTQNaRie5xb"},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dstH0Cm-fLgK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144414618,"user_tz":420,"elapsed":2986,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"2f05b456-85ef-463a-e699-9e1e6ff7dce8"},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_test[0]: mean: 0.1, std: 0.9\n","X_test[1]: mean: 0.0, std: 1.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IHofozO7RIiV"},"source":["# Linear model"]},{"cell_type":"markdown","metadata":{"id":"DlVmr5XkRMCf"},"source":["Before we get to our neural network, we're going to motivate non-linear activation functions by implementing a generalized linear model (logistic regression). We'll see why linear models (with linear activations) won't suffice for our dataset."]},{"cell_type":"code","metadata":{"id":"4K2qHbffAeGL"},"source":["import torch"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bn9Kr2XrAeCR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144417297,"user_tz":420,"elapsed":5647,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"726bfbeb-9474-46b1-8659-2decbc745a30"},"source":["# Set seed for reproducibility\n","torch.manual_seed(SEED)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch._C.Generator at 0x7f945dd4bdf8>"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"5YkMN-nU-gz7"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"hMzXPa6g-467"},"source":["from torch import nn\n","import torch.nn.functional as F"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RJlz9hEe_2xX"},"source":["INPUT_DIM = X_train.shape[1] # X is 2-dimensional\n","HIDDEN_DIM = 100\n","NUM_CLASSES = len(classes) # 3 classes"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oolBQZaE-43y"},"source":["class LinearModel(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(LinearModel, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = self.fc1(x_in) # linear activation\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q1aORSTK-41F","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144417299,"user_tz":420,"elapsed":5628,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"582fe193-702d-47f5-83d1-23e03f738152"},"source":["# Initialize model\n","model = LinearModel(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of LinearModel(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AUdgkiuRBZIo"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"SiqIcs6FBo70"},"source":["from torch.optim import Adam"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UmOydQRnBYqc"},"source":["LEARNING_RATE = 1e-2\n","NUM_EPOCHS = 10\n","BATCH_SIZE = 32"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qg8YRdv9AZ6p"},"source":["# Define Loss\n","class_weights_tensor = torch.Tensor(list(class_weights.values()))\n","loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NFwrk5E2AZ2g"},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dVA58YPNc2_f"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bpBgduAbadfa"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"47LEsByWc3bp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144417744,"user_tz":420,"elapsed":6046,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"26fe1bb9-6b74-40c1-c65e-cc7478de98ba"},"source":["# Training\n","for epoch in range(NUM_EPOCHS):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%1==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.13, accuracy: 49.9\n","Epoch: 1 | loss: 0.91, accuracy: 50.3\n","Epoch: 2 | loss: 0.79, accuracy: 55.3\n","Epoch: 3 | loss: 0.74, accuracy: 54.6\n","Epoch: 4 | loss: 0.74, accuracy: 53.7\n","Epoch: 5 | loss: 0.75, accuracy: 53.6\n","Epoch: 6 | loss: 0.76, accuracy: 53.7\n","Epoch: 7 | loss: 0.77, accuracy: 53.8\n","Epoch: 8 | loss: 0.77, accuracy: 53.9\n","Epoch: 9 | loss: 0.78, accuracy: 53.9\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_gIcdLFeCLR_"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"TyfL-k6uBzMW"},"source":["import json\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import precision_recall_fscore_support"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"55oQ-dCfgv2T"},"source":["def get_performance(y_true, y_pred, classes):\n","    \"\"\"Per-class performance metrics.\"\"\"\n","    # Performance\n","    performance = {\"overall\": {}, \"class\": {}}\n","\n","    # Overall performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n","    performance[\"overall\"][\"precision\"] = metrics[0]\n","    performance[\"overall\"][\"recall\"] = metrics[1]\n","    performance[\"overall\"][\"f1\"] = metrics[2]\n","    performance[\"overall\"][\"num_samples\"] = np.float64(len(y_true))\n","\n","    # Per-class performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=None)\n","    for i in range(len(classes)):\n","        performance[\"class\"][classes[i]] = {\n","            \"precision\": metrics[0][i],\n","            \"recall\": metrics[1][i],\n","            \"f1\": metrics[2][i],\n","            \"num_samples\": np.float64(metrics[3][i]),\n","        }\n","\n","    return performance"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Au0tESHKC8Rj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144417746,"user_tz":420,"elapsed":6030,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"4bee4ecb-51d6-409f-d898-ac58b210dd09"},"source":["# Predictions\n","y_prob = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {y_prob[0]}\")\n","y_pred = y_prob.max(dim=1)[1]\n","print (f\"sample class: {y_pred[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.8995, 0.0286, 0.0719], grad_fn=<SelectBackward>)\n","sample class: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LfvOtw6lgv5E","executionInfo":{"status":"ok","timestamp":1608144417747,"user_tz":420,"elapsed":6016,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"df2b1b8e-775e-40e6-9312-6980cac8e6ed"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.5326832791621524,\n","    \"recall\": 0.5333333333333333,\n","    \"f1\": 0.5327986224880954,\n","    \"num_samples\": 225.0\n","  },\n","  \"class\": {\n","    \"c1\": {\n","      \"precision\": 0.5,\n","      \"recall\": 0.5066666666666667,\n","      \"f1\": 0.5033112582781457,\n","      \"num_samples\": 75.0\n","    },\n","    \"c2\": {\n","      \"precision\": 0.5211267605633803,\n","      \"recall\": 0.49333333333333335,\n","      \"f1\": 0.5068493150684932,\n","      \"num_samples\": 75.0\n","    },\n","    \"c3\": {\n","      \"precision\": 0.5769230769230769,\n","      \"recall\": 0.6,\n","      \"f1\": 0.5882352941176471,\n","      \"num_samples\": 75.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gdzQ8LVuGJL3"},"source":["def plot_multiclass_decision_boundary(model, X, y):\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n","    cmap = plt.cm.Spectral\n","    \n","    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n","    y_pred = model(X_test, apply_softmax=True)\n","    _, y_pred = y_pred.max(dim=1)\n","    y_pred = y_pred.reshape(xx.shape)\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gGfevv1kusw8","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144418309,"user_tz":420,"elapsed":6560,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"9a289170-94e6-4de6-bb96-1bcfd1d172ea"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"R3OK8p-Ng3BC"},"source":["# Activation functions"]},{"cell_type":"markdown","metadata":{"id":"ghf5uLuhg3D0"},"source":["Using the generalized linear method (logistic regression) yielded poor results because of the non-linearity present in our data yet our activation functions were linear. We need to use an activation function that can allow our model to learn and map the non-linearity in our data. There are many different options so let's explore a few."]},{"cell_type":"code","metadata":{"id":"ivnfSKEhg3Md","colab":{"base_uri":"https://localhost:8080/","height":227},"executionInfo":{"status":"ok","timestamp":1608144418490,"user_tz":420,"elapsed":6725,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e5dfbff3-8adf-4d34-87b4-cb25e7c9ed4b"},"source":["# Fig size\n","plt.figure(figsize=(12,3))\n","\n","# Data\n","x = torch.arange(-5., 5., 0.1)\n","\n","# Sigmoid activation (constrain a value between 0 and 1.)\n","plt.subplot(1, 3, 1)\n","plt.title(\"Sigmoid activation\")\n","y = torch.sigmoid(x)\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Tanh activation (constrain a value between -1 and 1.)\n","plt.subplot(1, 3, 2)\n","y = torch.tanh(x)\n","plt.title(\"Tanh activation\")\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Relu (clip the negative values to 0)\n","plt.subplot(1, 3, 3)\n","y = F.relu(x)\n","plt.title(\"ReLU activation\")\n","plt.plot(x.numpy(), y.numpy())\n","\n","# Show plots\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x216 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"b_yo9fwwciDY"},"source":["The ReLU activation function ($max(0,z)$) is by far the most widely used activation function for neural networks. But as you can see, each activation function has its own constraints so there are circumstances where you'll want to use different ones. For example, if we need to constrain our outputs between 0 and 1, then the sigmoid activation is the best choice."]},{"cell_type":"markdown","metadata":{"id":"u72au7H9oHk7"},"source":["> In some cases, using a ReLU activation function may not be sufficient. For instance, when the outputs from our neurons are mostly negative, the activation function will produce zeros. This effectively creates a \"dying ReLU\" and a recovery is unlikely. To mitigate this effect, we could lower the learning rate or use [alternative ReLU activations](https://medium.com/@danqing/a-practical-guide-to-relu-b83ca804f1f7), ex. leaky ReLU or parametric ReLU (PReLU), which have a small slope for negative neuron outputs. "]},{"cell_type":"markdown","metadata":{"id":"xqWpi0X2dWpe"},"source":["# NumPy\n","\n","Now let's create our multilayer perceptron (MLP) which is going to be exactly like the logistic regression model but with the activation function to map the non-linearity in our data. \n","\n","> It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using PyTorch.\n"]},{"cell_type":"markdown","metadata":{"id":"0prEPVOlv1F4"},"source":["Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"pB1N3X34EHLS"},"source":["## Initialize weights"]},{"cell_type":"markdown","metadata":{"id":"a25Zg2yyvhKL"},"source":["1. Randomly initialize the model's weights $W$ (we'll cover more effective initialization strategies later in this lesson)."]},{"cell_type":"code","metadata":{"id":"Y3udiI1Idr62","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418491,"user_tz":420,"elapsed":6711,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"777ea575-11dd-43ba-928f-5351d17979d7"},"source":["# Initialize first layer's weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","print (f\"W1: {W1.shape}\")\n","print (f\"b1: {b1.shape}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["W1: (2, 100)\n","b1: (1, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oxF3FnErEKfr"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"CqllhIMgxGq7"},"source":["2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities."]},{"cell_type":"markdown","metadata":{"id":"pH9-D8YE4pHH"},"source":["First we pass the inputs into the first layer.\n","  * $z_1 = XW_1$"]},{"cell_type":"code","metadata":{"id":"TAeYeoMtdr-t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418492,"user_tz":420,"elapsed":6697,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"b317217d-0abf-415e-b202-d02282a165a2"},"source":["# z1 = [NX2] · [2X100] + [1X100] = [NX100]\n","z1 = np.dot(X_train, W1) + b1\n","print (f\"z1: {z1.shape}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["z1: (1050, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"15yZEt903YOO"},"source":["Next we apply the non-linear activation function, ReLU ($max(0,z)$) in this case.\n","  * $a_1 = f(z_1)$"]},{"cell_type":"code","metadata":{"id":"yAFau-Zm3YVM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418772,"user_tz":420,"elapsed":6964,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"df1030bb-8304-448d-9d34-8afe5d148447"},"source":["# Apply activation function\n","a1 = np.maximum(0, z1) # ReLU\n","print (f\"a_1: {a1.shape}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["a_1: (1050, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gNHCPVa03YGB"},"source":["We pass the activations to the second layer to get our logits.\n","  * $z_2 = a_1W_2$"]},{"cell_type":"code","metadata":{"id":"kyJS1y2o3YJb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418773,"user_tz":420,"elapsed":6952,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e0bc7d4f-b327-4181-b622-97c248c34b2d"},"source":["# Initialize second layer's weights\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","print (f\"W2: {W2.shape}\")\n","print (f\"b2: {b2.shape}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["W2: (100, 3)\n","b2: (1, 3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k_sCtbVh5DfI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418774,"user_tz":420,"elapsed":6939,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"4b383131-2904-459c-cbdc-4f09c7cabfb0"},"source":["# z2 = logits = [NX100] · [100X3] + [1X3] = [NX3]\n","logits = np.dot(a1, W2) + b2\n","print (f\"logits: {logits.shape}\")\n","print (f\"sample: {logits[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["logits: (1050, 3)\n","sample: [-0.00010001  0.00418463 -0.00067274]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6r_NhZuP3X5g"},"source":["We'll apply the softmax function to normalize the logits and btain class probabilities.\n","  * $\\hat{y} = softmax(z_2)$"]},{"cell_type":"code","metadata":{"id":"wI3jpHGJ3X_i","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144418774,"user_tz":420,"elapsed":6925,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"eb503e06-b8f3-4d21-c925-d6b362b96444"},"source":["# Normalization via softmax to obtain class probabilities\n","exp_logits = np.exp(logits)\n","y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","print (f\"y_hat: {y_hat.shape}\")\n","print (f\"sample: {y_hat[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["y_hat: (1050, 3)\n","sample: [0.33292037 0.33434987 0.33272975]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AhbLV8h7EPUT"},"source":["## Loss"]},{"cell_type":"markdown","metadata":{"id":"_VzjdcZG2h_h"},"source":["3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n","  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"]},{"cell_type":"code","metadata":{"id":"0llchiDhxGGI"},"source":["# Loss\n","correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","loss = np.sum(correct_class_logprobs) / len(y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OsLXL8rxEYE6"},"source":["## Gradients"]},{"cell_type":"markdown","metadata":{"id":"6y4ZWFp32jd2"},"source":["4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n","\n","The gradient of the loss w.r.t to $W_2$ is the same as the gradients from logistic regression since $\\hat{y} = softmax(z_2)$.\n","  * $\\frac{\\partial{J}}{\\partial{W_{2j}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}0 - e^{a_1W_{2y}}e^{a_1W_{2j}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{a_1e^{a_1W_{2j}}}{\\sum_j e^{a_1W}} = a_1\\hat{y}$\n","  * $\\frac{\\partial{J}}{\\partial{W_{2y}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}a_1 - e^{a_1W_{2y}}e^{a_1W_{2y}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{1}{\\hat{y}}(a_1\\hat{y} - a_1\\hat{y}^2) = a_1(\\hat{y}-1)$\n","\n","The gradient of the loss w.r.t $W_1$ is a bit trickier since we have to backpropagate through two sets of weights.\n","  * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_1}}  \\frac{\\partial{a_1}}{\\partial{z_1}}  \\frac{\\partial{z_1}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $"]},{"cell_type":"code","metadata":{"id":"9D2ujwNOxGQ1"},"source":["# dJ/dW2\n","dscores = y_hat\n","dscores[range(len(y_hat)), y_train] -= 1\n","dscores /= len(y_train)\n","dW2 = np.dot(a1.T, dscores)\n","db2 = np.sum(dscores, axis=0, keepdims=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gEpKpAgn78h4"},"source":["# dJ/dW1\n","dhidden = np.dot(dscores, W2.T)\n","dhidden[a1 <= 0] = 0 # ReLu backprop\n","dW1 = np.dot(X_train.T, dhidden)\n","db1 = np.sum(dhidden, axis=0, keepdims=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tKlRXHmoEafb"},"source":["## Update weights"]},{"cell_type":"markdown","metadata":{"id":"z1j-lgGq2lb_"},"source":["5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probability for the incorrect classes ($j$) and encourage a higher probability for the correct class ($y$).\n","  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$"]},{"cell_type":"code","metadata":{"id":"6kZoWyx-xGW8"},"source":["# Update weights\n","W1 += -LEARNING_RATE * dW1\n","b1 += -LEARNING_RATE * db1\n","W2 += -LEARNING_RATE * dW2\n","b2 += -LEARNING_RATE * db2"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7K8S9rf1EdHy"},"source":["## Training"]},{"cell_type":"markdown","metadata":{"id":"6lho6k572mge"},"source":["6. Repeat steps 2 - 4 until model performs well."]},{"cell_type":"code","metadata":{"id":"BFH0tHlZaoaS"},"source":["# Convert tensors to NumPy arrays\n","X_train = X_train.numpy()\n","y_train = y_train.numpy()\n","X_val = X_val.numpy()\n","y_val = y_val.numpy()\n","X_test = X_test.numpy()\n","y_test = y_test.numpy()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ScHz7h6OxGU_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144423695,"user_tz":420,"elapsed":11816,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"f18974b5-535b-4f50-fe4b-062b4a54f324"},"source":["# Initialize random weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","\n","# Training loop\n","for epoch_num in range(1000):\n","\n","    # First layer forward pass [NX2] · [2X100] = [NX100]\n","    z1 = np.dot(X_train, W1) + b1\n","\n","    # Apply activation function\n","    a1 = np.maximum(0, z1) # ReLU\n","\n","    # z2 = logits = [NX100] · [100X3] = [NX3]\n","    logits = np.dot(a1, W2) + b2\n","    \n","    # Normalization via softmax to obtain class probabilities\n","    exp_logits = np.exp(logits)\n","    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","\n","    # Loss\n","    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","    loss = np.sum(correct_class_logprobs) / len(y_train)\n","\n","    # show progress\n","    if epoch_num%100 == 0:\n","        # Accuracy\n","        y_pred = np.argmax(logits, axis=1)\n","        accuracy =  np.mean(np.equal(y_train, y_pred))\n","        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n","\n","    # dJ/dW2\n","    dscores = y_hat\n","    dscores[range(len(y_hat)), y_train] -= 1\n","    dscores /= len(y_train)\n","    dW2 = np.dot(a1.T, dscores)\n","    db2 = np.sum(dscores, axis=0, keepdims=True)\n","\n","    # dJ/dW1\n","    dhidden = np.dot(dscores, W2.T)\n","    dhidden[a1 <= 0] = 0 # ReLu backprop\n","    dW1 = np.dot(X_train.T, dhidden)\n","    db1 = np.sum(dhidden, axis=0, keepdims=True)\n","\n","    # Update weights\n","    W1 += -1e0 * dW1\n","    b1 += -1e0 * db1\n","    W2 += -1e0 * dW2\n","    b2 += -1e0 * db2"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0, loss: 1.099, accuracy: 0.349\n","Epoch: 100, loss: 0.545, accuracy: 0.687\n","Epoch: 200, loss: 0.247, accuracy: 0.903\n","Epoch: 300, loss: 0.142, accuracy: 0.949\n","Epoch: 400, loss: 0.099, accuracy: 0.974\n","Epoch: 500, loss: 0.076, accuracy: 0.986\n","Epoch: 600, loss: 0.062, accuracy: 0.990\n","Epoch: 700, loss: 0.052, accuracy: 0.994\n","Epoch: 800, loss: 0.046, accuracy: 0.995\n","Epoch: 900, loss: 0.041, accuracy: 0.995\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5Ds6s29xiDgx"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"2hDGiO6Ng8CI"},"source":["class MLPFromScratch():\n","    def predict(self, x):\n","        z1 = np.dot(x, W1) + b1\n","        a1 = np.maximum(0, z1)\n","        logits = np.dot(a1, W2) + b2\n","        exp_logits = np.exp(logits)\n","        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","        return y_hat"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dv5Y3-1ibuPM"},"source":["# Evaluation\n","model = MLPFromScratch()\n","y_prob = model.predict(X_test)\n","y_pred = np.argmax(y_prob, axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XfmYidNSgkcv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144423700,"user_tz":420,"elapsed":11801,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"7495ce26-a8fb-4955-edde-d2cd79ea0b07"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.9956140350877193,\n","    \"recall\": 0.9955555555555556,\n","    \"f1\": 0.9955553580159119,\n","    \"num_samples\": 225.0\n","  },\n","  \"class\": {\n","    \"c1\": {\n","      \"precision\": 1.0,\n","      \"recall\": 0.9866666666666667,\n","      \"f1\": 0.9932885906040269,\n","      \"num_samples\": 75.0\n","    },\n","    \"c2\": {\n","      \"precision\": 1.0,\n","      \"recall\": 1.0,\n","      \"f1\": 1.0,\n","      \"num_samples\": 75.0\n","    },\n","    \"c3\": {\n","      \"precision\": 0.9868421052631579,\n","      \"recall\": 1.0,\n","      \"f1\": 0.9933774834437086,\n","      \"num_samples\": 75.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gesG_0FIdRom"},"source":["def plot_multiclass_decision_boundary_numpy(model, X, y, savefig_fp=None):\n","    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n","    Credit: https://cs231n.github.io/neural-networks-case-study/\n","\n","    Arguments:\n","        model {function} -- trained model with function model.predict(x_in).\n","        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n","        y {numpy.ndarray} -- 1D outputs with shape (N,).\n","    \"\"\"\n","    # Axis boundaries\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n","                         np.linspace(y_min, y_max, 101))\n","\n","    # Create predictions\n","    x_in = np.c_[xx.ravel(), yy.ravel()]\n","    y_pred = model.predict(x_in)\n","    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n","\n","    # Plot decision boundary\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())\n","\n","    # Plot\n","    if savefig_fp:\n","        plt.savefig(savefig_fp, format='png')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"I5pDl4hdgkaE","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144424494,"user_tz":420,"elapsed":12579,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"b60be794-2fe7-4a3d-9218-c0670e5c7f8c"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary_numpy(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary_numpy(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"br_O_NUCjkwm"},"source":["# PyTorch"]},{"cell_type":"markdown","metadata":{"id":"kNwK9qm6FpVm"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"ZZP6Tp6qY6m2"},"source":["We'll be using two linear layers along with PyTorch [Functional](https://pytorch.org/docs/stable/nn.functional.html) API's [ReLU](https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.relu) operation. "]},{"cell_type":"code","metadata":{"id":"YtZvQVVpvl5p"},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NwnvrAUGFrZW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144424495,"user_tz":420,"elapsed":12563,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"2b1fad8e-19fc-461e-b060-3ee54a9322fc"},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"o942ZstYFtHm"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"K5fIv-0sbpJ8"},"source":["# Define Loss\n","class_weights_tensor = torch.Tensor(list(class_weights.values()))\n","loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TN0Jh_drczUh"},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nmeU45XF0O-U"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SHKHbn4xFwjl"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SJc5obfebwEp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144424721,"user_tz":420,"elapsed":12766,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1ecc5264-c75d-4ac1-dc7e-a811489e2729"},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.11, accuracy: 24.3\n","Epoch: 10 | loss: 0.67, accuracy: 55.4\n","Epoch: 20 | loss: 0.51, accuracy: 70.6\n","Epoch: 30 | loss: 0.39, accuracy: 88.5\n","Epoch: 40 | loss: 0.29, accuracy: 90.3\n","Epoch: 50 | loss: 0.22, accuracy: 93.4\n","Epoch: 60 | loss: 0.18, accuracy: 94.7\n","Epoch: 70 | loss: 0.15, accuracy: 95.9\n","Epoch: 80 | loss: 0.12, accuracy: 97.3\n","Epoch: 90 | loss: 0.11, accuracy: 97.7\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"10HGpWGTF4oR"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"Y4tI2iZ1vl8e"},"source":["# Predictions\n","y_prob = model(X_test, apply_softmax=True)\n","y_pred = y_prob.max(dim=1)[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AztBDtaziBQZ","executionInfo":{"status":"ok","timestamp":1608144424723,"user_tz":420,"elapsed":12755,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"3f88da6f-1dac-4418-f5db-222313c55e9f"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.9913419913419913,\n","    \"recall\": 0.9911111111111112,\n","    \"f1\": 0.9911095305832148,\n","    \"num_samples\": 225.0\n","  },\n","  \"class\": {\n","    \"c1\": {\n","      \"precision\": 1.0,\n","      \"recall\": 0.9733333333333334,\n","      \"f1\": 0.9864864864864865,\n","      \"num_samples\": 75.0\n","    },\n","    \"c2\": {\n","      \"precision\": 1.0,\n","      \"recall\": 1.0,\n","      \"f1\": 1.0,\n","      \"num_samples\": 75.0\n","    },\n","    \"c3\": {\n","      \"precision\": 0.974025974025974,\n","      \"recall\": 1.0,\n","      \"f1\": 0.9868421052631579,\n","      \"num_samples\": 75.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Xj7cwzZ4NoVI","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144425036,"user_tz":420,"elapsed":13056,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"a8311930-6f12-466c-c4bc-20a68c2545f2"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"6Cv3ev9Uo1Ii"},"source":["## Inference"]},{"cell_type":"code","metadata":{"id":"h25pUbR8o9eF","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1608144425037,"user_tz":420,"elapsed":13045,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1aae49c4-9af6-4453-88e4-2e017a6623ee"},"source":["# Inputs for inference\n","X_infer = pd.DataFrame([{'X1': 0.1, 'X2': 0.1}])\n","X_infer.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.1</td>\n","      <td>0.1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    X1   X2\n","0  0.1  0.1"]},"metadata":{"tags":[]},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"8G5lIYReo9l_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425038,"user_tz":420,"elapsed":13035,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"48affbd9-a13d-4b92-a009-547dc90740a2"},"source":["# Standardize\n","X_infer = X_scaler.transform(X_infer)\n","print (X_infer)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[0.29906749 0.30544029]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Uow5g2-1o9kF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425038,"user_tz":420,"elapsed":13024,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e6e59459-4871-4fcc-fbd2-c0072339305c"},"source":["# Predict\n","y_infer = model(torch.Tensor(X_infer), apply_softmax=True)\n","prob, _class = y_infer.max(dim=1)\n","label = label_encoder.inverse_transform(_class.detach().numpy())[0]\n","print (f\"The probability that you have {label} is {prob.detach().numpy()[0]*100.0:.0f}%\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The probability that you have c1 is 92%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gWv-RU46k2UL"},"source":["# Initializing weights"]},{"cell_type":"markdown","metadata":{"id":"1hDPBE0sk2mJ"},"source":["So far we have been initializing weights with small random values and this isn't optimal for convergence during training. The objective is to have weights that are able to produce outputs that follow a similar distribution across all neurons. We can do this by enforcing weights to have unit variance prior the affine and non-linear operations."]},{"cell_type":"markdown","metadata":{"id":"YxCm7FyRFRWK"},"source":["> A popular method is to apply [xavier initialization](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization), which essentially initializes the weights to allow the signal from the data to reach deep into the network. You may be wondering why we don't do this for every forward pass and that's a great question. We'll look at more advanced strategies that help with optimization like batch/layer normalization, etc. in future lessons. Meanwhile you can check out other initializers [here](https://pytorch.org/docs/stable/nn.init.html)."]},{"cell_type":"code","metadata":{"id":"Dj7apAxufge7"},"source":["from torch.nn import init"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U5lUR__TKvqA"},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","\n","    def init_weights(self):\n","        init.xavier_normal(self.fc1.weight, gain=init.calculate_gain('relu')) \n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FAyjh3bieLjn"},"source":["# Dropout"]},{"cell_type":"markdown","metadata":{"id":"z6kLwcBveLy5"},"source":["A great technique to have our models generalize (perform well on test data) is to increase the size of your data but this isn't always an option. Fortuntely, there are methods like regularization and dropout that can help create a more robust model. \n","\n","Dropout is a technique (used only during training) that allows us to zero the outputs of neurons. We do this for `dropout_p`% of the total neurons in each layer and it changes every batch. Dropout prevents units from co-adapting too much to the data and acts as a sampling strategy since we drop a different set of neurons each time.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/images/basics/neural-networks/dropout.png\" width=\"350\">\n","</div>\n","\n","* [Dropout: A Simple Way to Prevent Neural Networks from\n","Overfitting](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)"]},{"cell_type":"code","metadata":{"id":"g1E2bDj2HKpb"},"source":["DROPOUT_P = 0.1 # % of the neurons that are dropped each pass"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DS26SgeFgvG1"},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, dropout_p, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.dropout = nn.Dropout(dropout_p) # dropout\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","\n","    def init_weights(self):\n","        init.xavier_normal(self.fc1.weight, gain=init.calculate_gain('relu')) \n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) \n","        z = self.dropout(z) # dropout\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1he_g3eJgvOk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425043,"user_tz":420,"elapsed":13005,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1a42d44a-8be4-42a6-cb17-d467816edc31"},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, \n","            dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (fc2): Linear(in_features=100, out_features=3, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9ijvHwcZg8mO"},"source":["# Overfitting"]},{"cell_type":"markdown","metadata":{"id":"FIhvdD_zg8os"},"source":["Though neural networks are great at capturing non-linear relationships they are highly susceptible to overfitting to the training data and failing to generalize on test data. Just take a look at the example below where we generate completely random data and are able to fit a model with [$2*N*C + D$](https://arxiv.org/abs/1611.03530) hidden units. The training performance is good (~70%) but the overfitting leads to very poor test performance. We'll be covering strategies to tackle overfitting in future lessons."]},{"cell_type":"code","metadata":{"id":"uRdM16NhazJP"},"source":["NUM_EPOCHS = 500\n","NUM_SAMPLES_PER_CLASS = 50\n","LEARNING_RATE = 1e-1\n","HIDDEN_DIM = 2 * NUM_SAMPLES_PER_CLASS * NUM_CLASSES + INPUT_DIM # 2*N*C + D"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qf00Biq6g8ty","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425282,"user_tz":420,"elapsed":13230,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"7984f5ab-1475-416d-90a9-ac2d1859e4e7"},"source":["# Generate random data\n","X = np.random.rand(NUM_SAMPLES_PER_CLASS * NUM_CLASSES, INPUT_DIM)\n","y = np.array([[i]*NUM_SAMPLES_PER_CLASS for i in range(NUM_CLASSES)]).reshape(-1)\n","print (\"X: \", format(np.shape(X)))\n","print (\"y: \", format(np.shape(y)))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X:  (150, 2)\n","y:  (150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-bA9vK9SWkjh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425282,"user_tz":420,"elapsed":13219,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"cc101adf-7800-4821-a136-60dd5264db46"},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, train_size=TRAIN_SIZE)\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train: (105, 2), y_train: (105,)\n","X_val: (22, 2), y_val: (22,)\n","X_test: (23, 2), y_test: (23,)\n","Sample point: [0.52553355 0.33956916] → 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lYwc9mk1f5bm"},"source":["# Standardize the inputs (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ogTctxN3gt8M"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DDUW0xVMh8v8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144425286,"user_tz":420,"elapsed":13206,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"ddd3529b-a78d-4c93-fed8-8f45c36957a9"},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, \n","            dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=302, bias=True)\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (fc2): Linear(in_features=302, out_features=3, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6bIiiaOmhFiN"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zgayj4E1WksH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144426037,"user_tz":420,"elapsed":13944,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"d684e5aa-e215-46ef-c03a-818cd03b4a44"},"source":["# Training\n","for epoch in range(NUM_EPOCHS):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%20==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 1.15, accuracy: 37.1\n","Epoch: 20 | loss: 1.04, accuracy: 47.6\n","Epoch: 40 | loss: 0.98, accuracy: 51.4\n","Epoch: 60 | loss: 0.90, accuracy: 57.1\n","Epoch: 80 | loss: 0.87, accuracy: 59.0\n","Epoch: 100 | loss: 0.88, accuracy: 58.1\n","Epoch: 120 | loss: 0.84, accuracy: 64.8\n","Epoch: 140 | loss: 0.86, accuracy: 61.0\n","Epoch: 160 | loss: 0.81, accuracy: 64.8\n","Epoch: 180 | loss: 0.89, accuracy: 59.0\n","Epoch: 200 | loss: 0.91, accuracy: 60.0\n","Epoch: 220 | loss: 0.82, accuracy: 63.8\n","Epoch: 240 | loss: 0.86, accuracy: 59.0\n","Epoch: 260 | loss: 0.77, accuracy: 66.7\n","Epoch: 280 | loss: 0.82, accuracy: 67.6\n","Epoch: 300 | loss: 0.88, accuracy: 57.1\n","Epoch: 320 | loss: 0.81, accuracy: 61.9\n","Epoch: 340 | loss: 0.79, accuracy: 63.8\n","Epoch: 360 | loss: 0.80, accuracy: 61.0\n","Epoch: 380 | loss: 0.86, accuracy: 64.8\n","Epoch: 400 | loss: 0.77, accuracy: 64.8\n","Epoch: 420 | loss: 0.79, accuracy: 64.8\n","Epoch: 440 | loss: 0.81, accuracy: 65.7\n","Epoch: 460 | loss: 0.77, accuracy: 70.5\n","Epoch: 480 | loss: 0.80, accuracy: 67.6\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8QG4bPzshN8L"},"source":["# Predictions\n","y_prob = model(X_test, apply_softmax=True)\n","y_pred = y_prob.max(dim=1)[1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CHnnmEuSjN--","executionInfo":{"status":"ok","timestamp":1608144426038,"user_tz":420,"elapsed":13931,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"4941676b-5a4b-466e-800d-dfd99d0719f8"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.17857142857142858,\n","    \"recall\": 0.16666666666666666,\n","    \"f1\": 0.1722222222222222,\n","    \"num_samples\": 23.0\n","  },\n","  \"class\": {\n","    \"c1\": {\n","      \"precision\": 0.0,\n","      \"recall\": 0.0,\n","      \"f1\": 0.0,\n","      \"num_samples\": 7.0\n","    },\n","    \"c2\": {\n","      \"precision\": 0.2857142857142857,\n","      \"recall\": 0.25,\n","      \"f1\": 0.26666666666666666,\n","      \"num_samples\": 8.0\n","    },\n","    \"c3\": {\n","      \"precision\": 0.25,\n","      \"recall\": 0.25,\n","      \"f1\": 0.25,\n","      \"num_samples\": 8.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rpSoAEdGWku5","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608144426717,"user_tz":420,"elapsed":14599,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"f1ce7496-f8c7-4e26-f6f6-388bf669fd62"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"yppKnFIL6Vv7"},"source":["It's important that we experiment, starting with simple models that underfit (high bias) and improve it towards a good fit. Starting with simple models (linear/logistic regression) let's us catch errors without the added complexity of more sophisticated models (neural networks). "]},{"cell_type":"markdown","metadata":{"id":"WC0m-lMnE4Jp"},"source":["<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/images/basics/neural-networks/fit.png\" width=\"700\">\n","</div>"]}]}